Yu Fan Chen

1.4k total citations · 1 hit paper
13 papers, 700 citations indexed

About

Yu Fan Chen is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Mechanical Engineering. According to data from OpenAlex, Yu Fan Chen has authored 13 papers receiving a total of 700 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Artificial Intelligence, 6 papers in Computer Vision and Pattern Recognition and 4 papers in Mechanical Engineering. Recurrent topics in Yu Fan Chen's work include Reinforcement Learning in Robotics (6 papers), Robotic Path Planning Algorithms (5 papers) and Modular Robots and Swarm Intelligence (3 papers). Yu Fan Chen is often cited by papers focused on Reinforcement Learning in Robotics (6 papers), Robotic Path Planning Algorithms (5 papers) and Modular Robots and Swarm Intelligence (3 papers). Yu Fan Chen collaborates with scholars based in United States, Australia and France. Yu Fan Chen's co-authors include Jonathan P. How, Michael Everett, Miao Liu, Nazım Kemal Üre, John Vian, Ali‐akbar Agha‐mohammadi, Shayegan Omidshafiei, Girish Chowdhary, Shih‐Yuan Liu and Miao Liu and has published in prestigious journals such as IEEE Access, IEEE Control Systems and Journal of Intelligent & Robotic Systems.

In The Last Decade

Yu Fan Chen

13 papers receiving 669 citations

Hit Papers

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What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Socially aware motion planning with deep reinforcement learning

2017 463 citations Yu Fan Chen, Michael Everett et al. profile →

Peers

Countries citing papers authored by Yu Fan Chen

Since Specialization

Citations

This map shows the geographic impact of Yu Fan Chen's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Yu Fan Chen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yu Fan Chen more than expected).

Fields of papers citing papers by Yu Fan Chen

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Yu Fan Chen. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Yu Fan Chen. The network helps show where Yu Fan Chen may publish in the future.

Co-authorship network of co-authors of Yu Fan Chen

This figure shows the co-authorship network connecting the top 25 collaborators of Yu Fan Chen. A scholar is included among the top collaborators of Yu Fan Chen based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Yu Fan Chen. Yu Fan Chen is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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13 of 13 papers shown

#	Work	Indexed citations
1	Optimization of Heat Transfer Model Based on Greedy Algorithm 2021 ·Journal of Physics Conference Series ·(unknown), Yu Fan Chen, (unknown), (unknown)	2
2	Collision Avoidance in Pedestrian-Rich Environments With Deep Reinforcement Learning 2021 ·IEEE Access ·Michael Everett, Yu Fan Chen, Jonathan P. How	130
3	Socially aware motion planning with deep reinforcement learning breakdown → 2017 ·Yu Fan Chen, Michael Everett, Miao Liu, Jonathan P. How	463
4	Measurable Augmented Reality for Prototyping Cyberphysical Systems: A Robotics Platform to Aid the Hardware Prototyping and Performance Testing of Algorithms 2016 ·IEEE Control Systems ·Shayegan Omidshafiei, Ali‐akbar Agha‐mohammadi, Yu Fan Chen, Nazım Kemal Üre, Shih‐Yuan Liu, Brett T. Lopez, (unknown), Jonathan P. How, John Vian	15
5	Augmented dictionary learning for motion prediction 2016 ·Yu Fan Chen, Miao Liu, Jonathan P. How	12
6	Motion planning with diffusion maps 2016 ·Yu Fan Chen, Shih‐Yuan Liu, Miao Liu, Justin Miller, Jonathan P. How	18
7	MAR-CPS: Measurable Augmented Reality for Prototyping Cyber-Physical Systems 2015 ·DSpace@MIT (Massachusetts Institute of Technology) ·John Vian, (unknown), Shayegan Omidshafiei, Ali‐akbar Agha‐mohammadi, Yu Fan Chen, Nazım Kemal Üre, Jonathan P. How	9
8	MAR-CPS: Measurable Augmented Reality for Prototyping Cyber-Physical Systems 2015 ·Shayegan Omidshafiei, Ali‐akbar Agha‐mohammadi, Yu Fan Chen, Nazım Kemal Üre, Jonathan P. How, John Vian, (unknown)	18
9	Planning for large-scale multiagent problems via hierarchical decomposition with applications to UAV health management 2014 ·Yu Fan Chen, Nazım Kemal Üre, Girish Chowdhary, Jonathan P. How, John Vian	13
10	Decentralized learning-based planning for multiagent missions in the presence of actuator failures 2013 ·Nazım Kemal Üre, Girish Chowdhary, Yu Fan Chen, Mark Cutler, Jonathan P. How, John Vian	8
11	Decentralized Learning based Planning Multiagent Missions in Presence of Actuator Failures 2013 ·Nazım Kemal Üre, Girish Chowdhary, Yu Fan Chen, Mark Cutler, Jonathan P. How, John Vian	2
12	Distributed Learning for Planning Under Uncertainty Problems with Heterogeneous Teams 2013 ·Journal of Intelligent & Robotic Systems ·Nazım Kemal Üre, Girish Chowdhary, Yu Fan Chen, Jonathan P. How, John Vian	7
13	Distributed Learning for Large-scale Planning Under Uncertainty Problems with Heterogeneous Teams 2013 ·AIAA Guidance, Navigation, and Control (GNC) Conference ·Nazım Kemal Üre, Girish Chowdhary, Yu Fan Chen, Jonathan P. How, John Vian	3

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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